Relevant bibliographies by topics / Data mining Case studies

Academic literature on the topic 'Data mining Case studies'

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Published: 10 December 2022
Last updated: 29 January 2023

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Journal articles on the topic "Data mining Case studies"

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MELLI, GABOR, XINDONG WU, PAUL BEINAT, FRANCESCO BONCHI, LONGBING CAO, RONG DUAN, CHRISTOS FALOUTSOS, et al. "TOP-10 DATA MINING CASE STUDIES." International Journal of Information Technology & Decision Making 11, no. 02 (March 2012): 389–400. http://dx.doi.org/10.1142/s021962201240007x.

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We report on the panel discussion held at the ICDM'10 conference on the top 10 data mining case studies in order to provide a snapshot of where and how data mining techniques have made significant real-world impact. The tasks covered by 10 case studies range from the detection of anomalies such as cancer, fraud, and system failures to the optimization of organizational operations, and include the automated extraction of information from unstructured sources. From the 10 cases we find that supervised methods prevail while unsupervised techniques play a supporting role. Further, significant domain knowledge is generally required to achieve a completed solution. Finally, we find that successful applications are more commonly associated with continual improvement rather than by single "aha moments" of knowledge ("nugget") discovery.
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Lomax, Susan, and Sunil Vadera. "Case Studies in Applying Data Mining for Churn Analysis." International Journal of Conceptual Structures and Smart Applications 5, no. 2 (July 2017): 22–33. http://dx.doi.org/10.4018/ijcssa.2017070102.

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The advent of price and product comparison sites now makes it even more important to retain customers and identify those that might be at risk of leaving. The use of data mining methods has been widely advocated for predicting customer churn. This paper presents two case studies that utilize decision tree learning methods to develop models for predicting churn for a software company. The first case study aims to predict churn for organizations which currently have an ongoing project, to determine if organizations are likely to continue with other projects. While the second case study presents a more traditional example, where the aim is to predict organizations likely to cease being a subscriber to a service. The case studies include presentation of the accuracy of the models using a standard methodology as well as comparing the results with what happened in practice. Both case studies show the significant savings that can be made, plus potential increase in revenue by using decision tree learning for churn analysis.
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Rauch, Jan, and Milan Šimůnek. "Data Mining with Histograms and Domain Knowledge – Case Studies and Considerations*." Fundamenta Informaticae 166, no. 4 (April 26, 2019): 349–78. http://dx.doi.org/10.3233/fi-2019-1805.

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Tang, Yan. "Studies on Broad-Sense Sample Method in Data Mining." Advanced Materials Research 989-994 (July 2014): 1453–55. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.1453.

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With the development of science and technology, people pay more and more attention to the reliability of the products, especially in some special field, such as aerospace, military products, and some products of high reliability and long life. As a part that runs through the whole life cycle of products, reliability test provides an important source of data for the design, batch production and residual life assessment of the product development. For some expensive, new products put into use, they are not quite little in amount, having the characteristics of small sample. In this case, how to use the existing data to predict product life, reliability of calculating the reliability of a product more accurately and other related parameters is particularly important.
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Conrads, Paul, Ruby Daamen, and Edwin A. Roehl. "Maximizing Data-Collection Networks by Using Data-Mining Techniques – Case Studies in the Florida Everglades." Proceedings of the Water Environment Federation 2008, no. 12 (January 1, 2008): 4384–98. http://dx.doi.org/10.2175/193864708788752296.

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Ding, Jianwei. "Case Investigation Technology Based on Artificial Intelligence Data Processing." Journal of Sensors 2021 (October 26, 2021): 1–9. http://dx.doi.org/10.1155/2021/4942657.

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Through data mining technology, the hidden information behind a large amount of data is discovered, which can help various management services and provide scientific basis for leadership decision-making. It is an important subject of current police information research. This paper conducts in-depth research on the investigation analysis and decision-making of public security cases and proposes a case-based reasoning model based on two case databases. Moreover, this paper discusses in detail the use of data mining technology to automatically establish a case database, which is a useful exploration and practice for the public security department to establish a new and efficient case investigation auxiliary decision-making system. In addition, this paper studies the method of using data mining technology to assist in the establishment of a case database, analyzes the characteristics of traditional case storage methods, and constructs a case investigation model based on artificial intelligence data processing. The research results show that the model constructed in this paper has certain practical effects.
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Altuntas, Serkan, Turkay Dereli, and Andrew Kusiak. "Assessment of corporate innovation capability with a data-mining approach: industrial case studies." Computers & Industrial Engineering 102 (December 2016): 58–68. http://dx.doi.org/10.1016/j.cie.2016.10.018.

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Heripracoyo, Sulistyo. "Data Warehouse dan Data Mining Pendidikan Tinggi: Studi Kasus Kategori Undur Diri di Universitas Bina Nusantara." ComTech: Computer, Mathematics and Engineering Applications 3, no. 2 (December 1, 2012): 808. http://dx.doi.org/10.21512/comtech.v3i2.2309.

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Data warehouse and data mining is used to extract useful information and has a specific meaning and to develop a real relationship between some variables stored in the data/data warehouse. A data warehouse is appropriately designed and added a requirement to provide appropriate data and is useful in making better decisions. Hardware and software facilitate adequate access to such data, analyze and display the results interactively. Data mining software is a highly effective tool that can be used to interrogate the data contained in the data warehouse in order to find a relationship (Neary 1999). This study conducts some literature studies applies some models and case studies in a higher education institution, in terms of the benefits, functions and development. The case study conducted is objected to see the trend and prediction of the number of students who drop out (DO).
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Gozali, Elahe, Bahlol Rahimi, Malihe Sadeghi, and Reza Safdari. "Diagnosis of diseases using data mining." Medical Technologies Journal 1, no. 4 (November 29, 2017): 120–21. http://dx.doi.org/10.26415/2572-004x-vol1iss4p120-121.

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Introduction: In the information age, data are the most important asset for health organizations. In the case of using data in useful and optimal manner, they can become financial resources for organization. Data mining is an appropriate method to transform this potential value into strategic information. Data mining means extraction of hidden information, recognition of hidden relationships and patterns, and in general, discovery of useful knowledge at high volume. The objective of this review paper was to evaluate using data mining in diagnoses of diseases. Methods: This research is a review paper conducted based on a structured review of the papers published in Science Direct, Pubmed, Google Scholar, SID, Magiran (between years 2005 and 2015) and books related to using data mining in medical science and using it in diagnose of diseases with related keywords. Results: Nowadays, data mining is used in many medical science studies, including diagnosis of diseases, discovering the hidden patterns in data, and so on. New ideas such as discovery of Knowledge from Discovery and Data Mining Database, which includes data mining techniques, have found more popularity and they has becomedesired research tool for researchers. Researchers can use them to identify patterns and relationshipsamong great number of variables. Using them, researchers have been able to predict theresults obtained from one disease by using information stores available in databases. Several studies have indicated that data mining is used widely in diagnosis of diseases based on types of information (medical images, characteristics of patients, and so on), such as tuberculosis, types of cancers, infectious diseases, and diagnosis of anomalies rarely diagnosed by human (spots and particular points within aye, which is the symptom of onset of blindness resulting from diabetes), determining type of behavior with patients, and predicting the success rate of surgical surgeries, determining the success rate of therapeutic methods in coping with incurable diseases, and so on. Conclusion: One of the most important challenging topics in healthcare is transformation of raw clinical data into meaningful information following continuous generation of great number of data. In current competitive environment, health organizations using technologies such as data mining to improve healthcare quality will achieve success faster. Many of research centers in Iran are faced with large volume of information, which is not analyzed at all or will be time-consuming due to using traditional methods, even in the case of using analysis and converting them to knowledge. In light of using data mining and its implementation, health organizations can transform the data into a powerful and competitive tool and take new steps in preventing, diagnosing, treating, and providing high-quality services for clients.
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Pelt, Maurice, Konstantinos Stamoulis, and Asteris Apostolidis. "Data analytics case studies in the maintenance, repair and overhaul (MRO) industry." MATEC Web of Conferences 304 (2019): 04005. http://dx.doi.org/10.1051/matecconf/201930404005.

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Data analytics seems a promising approach to address the problem of unpredictability in MRO organizations. The Amsterdam University of Applied Sciences in cooperation with the aviation industry has initiated a two-year applied research project to explore the possibilities of data mining. More than 25 cases have been studied at eight different MRO enterprises. The CRISP-DM methodology is applied to have a structural guideline throughout the project. The data within MROs were explored and prepared. Individual case studies conducted with statistical and machine learning methods, were successfully to predict among others, the duration of planned maintenance tasks as well as the optimal maintenance intervals, the probability of the occurrence of findings during maintenance tasks.
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Dissertations / Theses on the topic "Data mining Case studies"

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Xu, Jie. "MINING STATIC AND DYNAMIC STRUCTURAL PATTERNS IN NETWORKS FOR KNOWLEDGE MANAGEMENT: A COMPUTATIONAL FRAMEWORK AND CASE STUDIES." Diss., Tucson, Arizona : University of Arizona, 2005. http://etd.library.arizona.edu/etd/GetFileServlet?file=file:///data1/pdf/etd/azu%5Fetd%5F1151%5F1%5Fm.pdf&type=application/pdf.

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Ben, Nasr Sana. "Mining and modeling variability from natural language documents : two case studies." Thesis, Rennes 1, 2016. http://www.theses.fr/2016REN1S013/document.

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L'analyse du domaine vise à identifier et organiser les caractéristiques communes et variables dans un domaine. Dans la pratique, le coût initial et le niveau d'effort manuel associés à cette analyse constituent un obstacle important pour son adoption par de nombreuses organisations qui ne peuvent en bénéficier. La contribution générale de cette thèse consiste à adopter et exploiter des techniques de traitement automatique du langage naturel et d'exploration de données pour automatiquement extraire et modéliser les connaissances relatives à la variabilité à partir de documents informels. L'enjeu est de réduire le coût opérationnel de l’analyse du domaine. Nous étudions l'applicabilité de notre idée à travers deux études de cas pris dans deux contextes différents: (1) la rétro-ingénierie des Modèles de Features (FMs) à partir des exigences réglementaires de sûreté dans le domaine de l’industrie nucléaire civil et (2) l’extraction de Matrices de Comparaison de Produits (PCMs) à partir de descriptions informelles de produits. Dans la première étude de cas, nous adoptons des techniques basées sur l’analyse sémantique, le regroupement (clustering) des exigences et les règles d'association. L'évaluation de cette approche montre que 69% de clusters sont corrects sans aucune intervention de l'utilisateur. Les dépendances entre features montrent une capacité prédictive élevée: 95% des relations obligatoires et 60% des relations optionnelles sont identifiées, et la totalité des relations d'implication et d'exclusion sont extraites. Dans la deuxième étude de cas, notre approche repose sur la technologie d'analyse contrastive pour identifier les termes spécifiques au domaine à partir du texte, l'extraction des informations pour chaque produit, le regroupement des termes et le regroupement des informations. Notre étude empirique montre que les PCMs obtenus sont compacts et contiennent de nombreuses informations quantitatives qui permettent leur comparaison. L'expérience utilisateur montre des résultats prometteurs et que notre méthode automatique est capable d'identifier 43% de features correctes et 68% de valeurs correctes dans des descriptions totalement informelles et ce, sans aucune intervention de l'utilisateur. Nous montrons qu'il existe un potentiel pour compléter ou même raffiner les caractéristiques techniques des produits. La principale leçon à tirer de ces deux études de cas, est que l’extraction et l’exploitation de la connaissance relative à la variabilité dépendent du contexte, de la nature de la variabilité et de la nature du texte
Domain analysis is the process of analyzing a family of products to identify their common and variable features. This process is generally carried out by experts on the basis of existing informal documentation. When performed manually, this activity is both time-consuming and error-prone. In this thesis, our general contribution is to address mining and modeling variability from informal documentation. We adopt Natural Language Processing (NLP) and data mining techniques to identify features, commonalities, differences and features dependencies among related products. We investigate the applicability of this idea by instantiating it in two different contexts: (1) reverse engineering Feature Models (FMs) from regulatory requirements in nuclear domain and (2) synthesizing Product Comparison Matrices (PCMs) from informal product descriptions. In the first case study, we adopt NLP and data mining techniques based on semantic analysis, requirements clustering and association rules to assist experts when constructing feature models from these regulations. The evaluation shows that our approach is able to retrieve 69% of correct clusters without any user intervention. Moreover, features dependencies show a high predictive capacity: 95% of the mandatory relationships and 60% of optional relationships are found, and the totality of requires and exclude relationships are extracted. In the second case study, our proposed approach relies on contrastive analysis technology to mine domain specific terms from text, information extraction, terms clustering and information clustering. Overall, our empirical study shows that the resulting PCMs are compact and exhibit numerous quantitative and comparable information. The user study shows that our automatic approach retrieves 43% of correct features and 68% of correct values in one step and without any user intervention. We show that there is a potential to complement or even refine technical information of products. The main lesson learnt from the two case studies is that the exploitability and the extraction of variability knowledge depend on the context, the nature of variability and the nature of text
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Madani, Farshad. "Opportunity Identification for New Product Planning: Ontological Semantic Patent Classification." PDXScholar, 2018. https://pdxscholar.library.pdx.edu/open_access_etds/4232.

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Intelligence tools have been developed and applied widely in many different areas in engineering, business and management. Many commercialized tools for business intelligence are available in the market. However, no practically useful tools for technology intelligence are available at this time, and very little academic research in technology intelligence methods has been conducted to date. Patent databases are the most important data source for technology intelligence tools, but patents inherently contain unstructured data. Consequently, extracting text data from patent databases, converting that data to meaningful information and generating useful knowledge from this information become complex tasks. These tasks are currently being performed very ineffectively, inefficiently and unreliably by human experts. This deficiency is particularly vexing in product planning, where awareness of market needs and technological capabilities is critical for identifying opportunities for new products and services. Total nescience of the text of patents, as well as inadequate, unreliable and untimely knowledge derived from these patents, may consequently result in missed opportunities that could lead to severe competitive disadvantage and potentially catastrophic loss of revenue. The research performed in this dissertation tries to correct the abovementioned deficiency with an approach called patent mining. The research is conducted at Finex, an iron casting company that produces traditional kitchen skillets. To 'mine' pertinent patents, experts in new product development at Finex modeled one ontology for the required product features and another for the attributes of requisite metallurgical enabling technologies from which new product opportunities for skillets are identified by applying natural language processing, information retrieval, and machine learning (classification) to the text of patents in the USPTO database. Three main scenarios are examined in my research. Regular classification (RC) relies on keywords that are extracted directly from a group of USPTO patents. Ontological classification (OC) relies on keywords that result from an ontology developed by Finex experts, which is evaluated and improved by a panel of external experts. Ontological semantic classification (OSC) uses these ontological keywords and their synonyms, which are extracted from the WordNet database. For each scenario, I evaluate the performance of three classifiers: k-Nearest Neighbor (k-NN), random forest, and Support Vector Machine (SVM). My research shows that OSC is the best scenario and SVM is the best classifier for identifying product planning opportunities, because this combination yields the highest score in metrics that are generally used to measure classification performance in machine learning (e.g., ROC-AUC and F-score). My method also significantly outperforms current practice, because I demonstrate in an experiment that neither the experts at Finex nor the panel of external experts are able to search for and judge relevant patents with any degree of effectiveness, efficiency or reliability. This dissertation provides the rudiments of a theoretical foundation for patent mining, which has yielded a machine learning method that is deployed successfully in a new product planning setting (Finex). Further development of this method could make a significant contribution to management practice by identifying opportunities for new product development that have been missed by the approaches that have been deployed to date.
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Šenovský, Jakub. "Dolování z dat v jazyce Python." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2017. http://www.nusl.cz/ntk/nusl-363895.

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The main goal of this thesis was to get acquainted with the phases of data mining, with the support of the programming languages Python and R in the field of data mining and demonstration of their use in two case studies. The comparison of these languages in the field of data mining is also included. The data preprocessing phase and the mining algorithms for classification, prediction and clustering are described here. There are illustrated the most significant libraries for Python and R. In the first case study, work with time series was demonstrated using the ARIMA model and Neural Networks with precision verification using a Mean Square Error. In the second case study, the results of football matches are classificated using the K - Nearest Neighbors, Bayes Classifier, Random Forest and Logical Regression. The precision of the classification is displayed using Accuracy Score and Confusion Matrix. The work is concluded with the evaluation of the achived results and suggestions for the future improvement of the individual models.
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Pena, Isis. "Utility-based data mining: An anthropometric case study." Thesis, University of Ottawa (Canada), 2008. http://hdl.handle.net/10393/27723.

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One of the most important challenges for the apparel industry is to produce garments that fit the population properly. In order to achieve this objective, it is crucial to understand the typical profile of consumer's bodies. In this work, we aim to identify the typical consumer from the virtual tailor's perspective. To this end, we perform clustering analysis on anthropometric and 3-D data to group the population into clothing sizes. Next, we perform multi-view relational classification to analyze the interplay of different body measurements within each size. In this study, we analyze three different populations as contained in the CAESAR(TM) database, namely, the American, the Italian and the Dutch populations. Throughout this study, we follow a utility-based data mining approach. The goal of utility-base data mining is to consider all utility aspects of the mining process and to thus maximize the utility of the entire process. In order to address this issue, we engage in dimension reduction techniques to find a smaller set of body measurement that reduces the cost and improves the performance of the mining process. We also apply objective interestingness measures in our analysis of demographic data, to improve the quality of the results and reduce the time and search space of the mining process. The analysis of demographic data allows us to better understand the demographic nature of potential customers, in order to target subgroups of potential customers better.
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Daley, Caitlin Marie. "Application of Data Mining Tools for Exploring Data: Yarn Quality Case Study." NCSU, 2008. http://www.lib.ncsu.edu/theses/available/etd-10292008-165755/.

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Businesses are constantly striving for a competitive edge in the economy, and data-driven decision making is crucial to achieve this goal. Four data mining tools, principal component analysis, cluster analysis, recursive partitioning, and discriminant analysis, were used to explore the major factors that contribute to ends down in a rotor spinning manufacturing process. Principal component analysis was used to explore the research question about whether the large number of cotton properties used to classify cotton could be reduced to a significant few. Cluster analysis was used to gain insight about whether there were groups of gins, counties, or classing offices that produced better raw material than others and led to less ends down. The important research question of what raw material properties were affecting ends down was explored with both recursive partitioning and discriminant analysis. Additional research investigated the effect of cotton variety and atmospheric conditions on spinning productivity. Each of the four data mining tools used was informative and offered a different perspective to the overall research question. Several significant factors emerged including humidity, temperature, %DP 555, and uniformity in addition to micronaire and the color properties (+b and Rd). With these results the researcher developed an improvement plan for better control and increased spinning productivity in future operations. A designed experiment is necessary to thoroughly investigate the impact of certain factors beyond the exploratory conclusions obtained from this study.
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Ivanovskiy, Tim V. "Mining Medical Data in a Clinical Environment." Scholar Commons, 2006. http://scholarcommons.usf.edu/etd/3908.

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The availability of new treatments for a disease depends on the success of clinical trials. In order for a clinical trial to be successful and approved, medical researchers must first recruit patients with a specific set of conditions in order to test the effectiveness of the proposed treatment. In the past, the accrual process was tedious and time-consuming. Since accruals rely heavily on the ability of physicians and their staff to be familiar with the protocol eligibility criteria, candidates tend to be missed. This can result and has resulted in unsuccessful trials.A recent project at the University of South Florida aimed to assist research physicians at H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida, with a screening process by utilizing a web-based expert system, Moffitt Expedited Accrual Network System (MEANS). This system allows physicians to determine the eligibility of a patient for several clinical trials simultaneously.We have implemented this web-based expert system at the H. Lee Moffitt Cancer Center & Research Gastroenterology (GI) Clinic. Based on our findings and staff feedback, the system has undergone many optimizations. We used data mining techniques to analyze the medical data of current gastrointestinal patients. The use of the Apriori algorithm allowed us to discover new rules (implications) in the patient data. All of the discovered implications were checked for medical validity by a physician, and those that were determined to be valid were entered into the expert system. Additional analysis of the data allowed us to streamline the system and decrease the number of mouse clicks required for screening. We also used a probability-based method to reorder the questions, which decreased the amount of data entry required to determine a patient's ineligibility.
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桂宏胜 and Hongsheng Gui. "Data mining of post genome-wide association studies and next generation sequencing." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2013. http://hdl.handle.net/10722/193431.

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Damle, Chaitanya. "Flood forecasting using time series data mining." [Tampa, Fla.] : University of South Florida, 2005. http://purl.fcla.edu/fcla/etd/SFE0001038.

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Haneuse, Sebastian J. P. A. "Ecological studies using supplemental case-control data /." Thesis, Connect to this title online; UW restricted, 2004. http://hdl.handle.net/1773/9595.

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Books on the topic "Data mining Case studies"

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Torgo, Luís. Data mining with R: Learning with case studies. Boca Raton: Chapman & Hall/CRC, 2011.

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Innovative applications in data mining. Berlin: Springer, 2009.

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Kirt, Toomas. Concept formation in exploratory data analysis: Case studies of linguistic and banking data. Tallinn: TUT Press, 2007.

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Petr, Berka, Rauch Jan, and Zighed Djamel A. 1955-, eds. Data mining and medical knowledge management: Cases and applications. Hershey PA: Information Science Reference, 2009.

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Yeo, Ai Cheo. Implementing a data mining solution for an automobile insurance company: Reconciling theoretical benefits with practical considerations. Hershey, PA: Idea Group, 2003.

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Kneisley, R. O. Microseismic data analysis of failure occurrence in a deep, western U.S. coal mine: A case study. Pittsburgh, PA: U.S. Dept. of the Interior, Bureau of Mines, 1989.

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Kneisley, R. O. Microseismic data analysis of failure occurrence in a deep, western U.S. coal mine: A case study. Washington, DC: Dept. of the Interior, 1989.

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Sabol, Tomáš. Foreign direct investments in Central East Europe and their impact on productivity gap--analysis using statistical and data mining approach: Some results of the Productivity Gap Project. Košice: Vienala, 2005.

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Gentleman, Jane F., and G. A. Whitmore, eds. Case Studies in Data Analysis. New York, NY: Springer New York, 1994. http://dx.doi.org/10.1007/978-1-4612-2688-8.

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Shipman, Alan. Data protection: Risk assessment case studies. London: BSI Business Information, 2004.

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Book chapters on the topic "Data mining Case studies"

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Maimon, Oded, and Mark Last. "Case Studies." In Knowledge Discovery and Data Mining, 71–103. Boston, MA: Springer US, 2001. http://dx.doi.org/10.1007/978-1-4757-3296-2_6.

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Vazirgiannis, Michalis, Maria Halkidi, and Dimitrios Gunopulos. "Case Studies." In Uncertainty Handling and Quality Assessment in Data Mining, 199–221. London: Springer London, 2003. http://dx.doi.org/10.1007/978-1-4471-0031-7_6.

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Agarwal, Sonali, Murli Dhar Tiwari, and Iti Tiwari. "Government Data Mining Case Studies on Education and Health." In E Governance Data Center, Data Warehousing and Data Mining, 155–201. New York: River Publishers, 2022. http://dx.doi.org/10.1201/9781003357254-8.

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Fares, Ahmed, João Gama, and Pedro Campos. "Process Mining for Analyzing Customer Relationship Management Systems: A Case Study." In Studies in Big Data, 209–21. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-89803-2_9.

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Löffler, Ralf. "Opinion Mining from Unstructured Web 2.0 Data: A Dicode Use Case." In Studies in Big Data, 181–200. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-02612-1_9.

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Bhushan, A., and U. Bellur. "Mining Swarms from Moving Object Data Streams." In Geospatial Infrastructure, Applications and Technologies: India Case Studies, 271–84. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2330-0_20.

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Fournier-Viger, Philippe, Roger Nkambou, Usef Faghihi, and Engelbert Mephu Nguifo. "Mining Temporal Patterns to Improve Agents Behavior: Two Case Studies." In Data Mining and Multi-agent Integration, 77–92. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-1-4419-0522-2_5.

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Shekhar, Shashi, Yan Huang, Weili Wu, C. T. Lu, and S. Chawla. "What’s Spatial About Spatial Data Mining: Three Case Studies." In Data Mining for Scientific and Engineering Applications, 487–514. Boston, MA: Springer US, 2001. http://dx.doi.org/10.1007/978-1-4615-1733-7_26.

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Tsumoto, Shusaku. "Clinical Knowledge Discovery in Hospital Information Systems: Two Case Studies." In Principles of Data Mining and Knowledge Discovery, 652–56. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-45372-5_80.

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Dahan, Haim, Shahar Cohen, Lior Rokach, and Oded Maimon. "Proactive Data Mining in the Real World: Case Studies." In SpringerBriefs in Electrical and Computer Engineering, 35–61. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-0539-3_4.

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Conference papers on the topic "Data mining Case studies"

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Gertsbakh, I., I. Yatskiv, and O. Platonova. "Constructing social and economic indicators for EU countries using dynamic classification: case studies." In DATA MINING 2008. Southampton, UK: WIT Press, 2008. http://dx.doi.org/10.2495/data080151.

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Dossis, Michael, Dimitrios Amanatidis, and Ifigeneia Mylona. "Mining Twitter Data: Case Studies with Trending Hashtags." In The 4th Virtual International Conference on Advanced Research in Scientific Areas. Publishing Society, 2015. http://dx.doi.org/10.18638/arsa.2015.4.1.751.

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Whalen, Sean, and Gaurav Pandey. "A Comparative Analysis of Ensemble Classifiers: Case Studies in Genomics." In 2013 IEEE International Conference on Data Mining (ICDM). IEEE, 2013. http://dx.doi.org/10.1109/icdm.2013.21.

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"Preface to the Fourth Workshop on Data Mining Case Studies." In 2011 IEEE International Conference on Data Mining Workshops (ICDMW). IEEE, 2011. http://dx.doi.org/10.1109/icdmw.2011.198.

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Sweeney, Mack, Matthew van Adelsberg, Kathryn Laskey, and Carlotta Domeniconi. "Effects of Model Misspecification on BayesianBandits: Case Studies in UX Optimization." In 2020 IEEE International Conference on Data Mining (ICDM). IEEE, 2020. http://dx.doi.org/10.1109/icdm50108.2020.00165.

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Kitts, Brendan, Gabor Melli, Gregory Piatetsky-Shapiro, Pip Courbois, Simeon J. Simoff, Jing Ying Zhang, Karl Rexer, et al. "Second Workshop on Data Mining Case Studies and Practice Prize." In KDD07: The 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2007. http://dx.doi.org/10.1145/1281192.1327955.

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Kitts, Brendan, Gabor Melli, Gregory Piatetsky-Shapiro, Pip Courbois, Simeon J. Simoff, Jing Ying Zhang, Karl Rexer, et al. "Second Workshop on Data Mining Case Studies and Practice Prize." In KDD07: The 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2007. http://dx.doi.org/10.1145/1327942.1327955.

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Curet, O. "Designing knowledge discovery based systems in business, finance and accounting with a case-based approach: two case studies." In IEE Colloquium on Knowledge Discovery and Data Mining. IEE, 1996. http://dx.doi.org/10.1049/ic:19961107.

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Lloyd-Williams, M. "Case studies in the data mining approach to health information analysis." In IEE Two-day Colloquium on Knowledge Discovery and Data Mining. IEE, 1998. http://dx.doi.org/10.1049/ic:19980641.

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Shahin, Ahmad, Walid Moudani, Fadi Chakik, and Mohamad Khalil. "Data mining in healthcare information systems: Case studies in Northern Lebanon." In 2014 Third International Conference on e-Technologies and Networks for Development (ICeND). IEEE, 2014. http://dx.doi.org/10.1109/icend.2014.6991370.

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Reports on the topic "Data mining Case studies"

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Navas-Alemán, Lizbeth. Innovation and Competitiveness in Mining Value Chains: The Case of Brazil. Inter-American Development Bank, December 2021. http://dx.doi.org/10.18235/0003813.

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Mining companies have mirrored other large multinational companies in setting up global value chains (GVCs), sourcing their inputs and services from an ever-larger number of highly capable suppliers in developing countries, such as those in resource-rich Latin America. However, recent empirical studies on the mining GVC in that region suggest that even innovative local suppliers find it difficult to exploit their innovations in local and foreign markets. Using a conceptual framework that combines literature on innovation and GVCs, this study analyzed how global/regional- and firm-level factors interact to explain the acquisition of local suppliers capabilities within Brazils mining industry. The study explored these issues using original data gathered in 2019 and secondary sources from Brazil. The main findings are related to (i) strategies used by domestic suppliers to develop innovative solutions for leading mining companies, (ii) how health and safety concerns spurred innovation after the disasters in Mariana and Brumadinho, (iii) new-to-the-world innovation capabilities among Brazilian suppliers to the mining industry, and (iv) the main barriers to developing innovative practices among domestic suppliers. The authors propose public policies to support major mining companies in acquiring innovations from domestic suppliers to the mining industry. Opportunities such as a Copper Rush in Brazil that could foster further innovations in mining are discussed.
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Mazzella, Andrew J., Delorey Jr., Larson Dennis E., Dickson Kevin P., and Jr Peter. Case Studies in Data Analysis. Fort Belvoir, VA: Defense Technical Information Center, June 1989. http://dx.doi.org/10.21236/ada215342.

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Vilhuber, Lars. Adjusting Imperfect Data: Overview and Case Studies. Cambridge, MA: National Bureau of Economic Research, March 2007. http://dx.doi.org/10.3386/w12977.

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Mittleman, Murray, Elizabeth Mostofsky, and Long Ngo. Improving Methods for Analyzing Data from Case-Only Studies. Patient-Centered Outcomes Research Institute® (PCORI), February 2022. http://dx.doi.org/10.25302/02.2022.me.150731028.

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Labrie, D., Y. Lizotte, and S. Dufresne. Common data bases for underground mining blast fragmentation and stability studies. Natural Resources Canada/CMSS/Information Management, 1991. http://dx.doi.org/10.4095/328735.

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Labrie, D., Y. Lizotte, and S. Dufresne. Common data bases for underground mining blast fragmentation and stability studies. Natural Resources Canada/CMSS/Information Management, 1991. http://dx.doi.org/10.4095/328739.

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Eyermann, T. J., L. L. Van Sambeek, and F. D. Hansen. Case studies of sealing methods and materials used in the salt and potash mining industries. Office of Scientific and Technical Information (OSTI), November 1995. http://dx.doi.org/10.2172/188537.

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Kamath, C., J. Franzman, and R. Ponmalai. Data Mining for Faster, Interpretable Solutions to Inverse Problems:A Case Study Using Additive Manufacturing. Office of Scientific and Technical Information (OSTI), January 2021. http://dx.doi.org/10.2172/1763188.

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Xie, Grace. Data Mining of the Rocky Flats Library Archive for Plutonium Compatibility Studies. Office of Scientific and Technical Information (OSTI), August 2022. http://dx.doi.org/10.2172/1881789.

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Xu, Tengfang, and Steve Greenberg. Data Center Energy Benchmarking: Part 5 - Case Studies on aCorporate Data Center (No. 22). Office of Scientific and Technical Information (OSTI), August 2007. http://dx.doi.org/10.2172/926604.

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